Asthma is a common disease with a prominent genetic component. Genetic studies have identified many sequence variants associated with asthma and other diseases. The human genetics community now faces the major challenge of moving from associated variants to causal variants. Addressing this challenge requires improved strategies for identifying how changes in gene sequence affect gene function. Most genetic variants associated with asthma and other common diseases are found in non-coding regions, but our understanding of how non-coding variants affect gene function remains very limited. This project focuses on dramatically advancing our ability to measure and predict how variants in 3' untranslated regions (3' UTRs) affect gene regulation. 3' UTRs contain cis-regulatory elements that control mRNA stability and translation by binding to specific proteins and miRNAs. We understand so little about 3' UTR function that it is impossible to accurately predict which 3' UTR variants affect gene function. We developed a novel massively parallel experimental method for functional annotation of sequences from three-prime UTRs (fast-UTR). Fast-UTR simultaneously measures the effects of very large numbers of 3' UTR sequence variants on mRNA levels, mRNA stability, and protein production in cells of interest. We propose to apply this method to understanding the role of 3' UTR sequence variation in asthma. In aim 1, we will select appropriate cellular models that represent key cell types known to be important in asthma and miRNAs that are abundant in those cells. In aim 2, we will use fast-UTR to study hundreds of thousands of 3' UTR variants found in the 1000 Genomes Project and the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) study. The CAAPA study includes whole genome sequencing of samples from 1005 highly diverse individuals from understudied populations with a high incidence of asthma. In aim 3, we will use fast-UTR results to improve methods for predicting effects of any 3' UTR variant and we will develop and implement methods for using fast-UTR fine-mapping information to help analyze cis-eQTL studies, the CAAPA study, and other GWAS and sequencing studies. This project will demonstrate how massively parallel assay technology can be harnessed to identify functional variants thereby addressing one of the most pressing issues facing the human genetics community today.